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IQ.Pilot Release Commit @ bec7652

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IQ.Lvbs CI [bot]
2026-08-22 21:29:55 -05:00
parent 11cefcb266
commit 7786d94c33
58 changed files with 578 additions and 267 deletions
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2
iqpilot/selfdrive/iqmodeld/models/__init__.py
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@@ -1,3 +1,3 @@
"""
IQ model selection and runner support that is actively used by iqmodeld.
Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
"""

7
iqpilot/selfdrive/iqmodeld/models/fetcher.py
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@@ -1,11 +1,8 @@
#!/usr/bin/env python3
"""
Copyright (c) IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos
Public entry point for the model-manifest fetcher: prefers the compiled private
bundle, falling back to the in-tree source. The default-runner fallback lives in
ManifestDecoder now, so no post-import patching is needed.
Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
"""
from iqpilot._proprietary_loader import ProprietaryModuleMissing, load_private_module
try:

6
iqpilot/selfdrive/iqmodeld/models/helpers.py
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@@ -40,7 +40,6 @@ _DEFAULT_BUNDLE_REF = "default"
def get_default_model_bundle(_bundles):
"""Legacy compatibility hook: stock default is preinstalled, not a manifest bundle."""
return None
@@ -239,10 +238,13 @@ def select_default_model(params: Params = None) -> None:
def seed_default_bundle_if_unset(params: Params = None) -> None:
params = Params() if params is None else params
if params.get(_ACTIVE_BUNDLE_KEY) or params.get(_DOWNLOAD_INDEX_KEY) is not None:
if params.get(_ACTIVE_BUNDLE_KEY):
return
queued_download = params.get(_DOWNLOAD_INDEX_KEY)
try:
select_default_model(params)
if queued_download is not None:
params.put(_DOWNLOAD_INDEX_KEY, queued_download)
cloudlog.warning("default_model: seeded Default (CD210) as active bundle")
except Exception as e:
cloudlog.exception(f"default_model: failed to seed default bundle: {e}")

4
iqpilot/selfdrive/iqmodeld/models/inference_state.py
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@@ -1,9 +1,5 @@
"""
Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos
Common base for the per-process inference/runtime states. It seeds the lateral
steer delay from the cached learned value so every subclass starts with a usable
number before its first lateralDelay message arrives.
"""
from iqpilot.common.steer_delay import cached_steer_delay

2
iqpilot/selfdrive/iqmodeld/models/runners/__init__.py
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@@ -1,3 +1,3 @@
"""
Runner interfaces used by iqmodeld model execution.
Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
"""

4
iqpilot/selfdrive/iqmodeld/models/runners/tinygrad/__init__.py
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@@ -1,3 +1,3 @@
"""
Tinygrad runner support for iqmodeld.
"""
Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
"""

7
iqpilot/selfdrive/iqmodeld/models/runners/tinygrad/fused_runner.py
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@@ -27,8 +27,6 @@ WARP_DEV = os.getenv('WARP_DEV')
class TinygradFusedRunner(ModelRunner):
"""Runs a fused warp+vision+policy pkl. Bundle ships one `driving_fused_*` artifact."""
uses_opencl_warp: bool = False
def __init__(self):
@@ -110,7 +108,6 @@ class TinygradFusedRunner(ModelRunner):
'feat_q': zeros_f32((self._frame_skip * (fb[1] - 1) + 1, fb[0], fb[2])),
'desire_q': zeros_f32((self._frame_skip * dp[1], dp[0], dp[2])),
}
# shapes must match the captured run_policy JIT inputs
on_shapes = self._on_meta['input_shapes']
captured = self._run_policy.captured
jit_shapes = {
@@ -135,7 +132,6 @@ class TinygradFusedRunner(ModelRunner):
self._cam_resolution = (cam_w, cam_h)
def run_fused(self, bufs: dict, transforms: dict[str, np.ndarray], numpy_inputs: NumpyDict) -> NumpyDict:
"""warp + vision + policy in one pass from raw NV12 bufs + transform matrices."""
Tensor, Device = _tinygrad_imports()
main_buf = bufs['img']
@@ -154,7 +150,6 @@ class TinygradFusedRunner(ModelRunner):
npy = lambda key: Tensor(self._npy_buffers[key], device='NPY')
# frames go on the compute device to match the captured warp JIT
frame = self._frame_tensor('img', bufs['img'])
big_frame = self._frame_tensor('big_img', bufs['big_img'])
@@ -169,8 +164,6 @@ class TinygradFusedRunner(ModelRunner):
policy_inputs['action_t'] = npy('action_t')
vision_out_t, on_out_t, off_out_t = self._run_policy(**policy_inputs)
# parse each model's output on its own sliced dict; parsing a merged dict
# would run parse_dynamic_outputs twice and double-parse plan/lead
def _slice(tensor_out, meta) -> NumpyDict:
flat = tensor_out.numpy().flatten()
return {k: flat[np.newaxis, sl] for k, sl in meta['output_slices'].items() if k != 'pad'}

7
iqpilot/selfdrive/iqmodeld/models/runners/tinygrad/supercombo_runner.py
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@@ -68,8 +68,6 @@ def _is_jit_arg_mismatch(err: BaseException) -> bool:
class TinygradSupercomboRunner(ModelRunner):
"""Runs a single combined supercombo pkl. Bundle ships one `driving_supercombo_*` artifact."""
uses_opencl_warp: bool = False
def __init__(self):
@@ -282,7 +280,6 @@ class TinygradSupercomboRunner(ModelRunner):
zeros_u8 = lambda s: Tensor(np.zeros(s, dtype=np.uint8), device=Device.DEFAULT).contiguous().realize()
zeros_f32 = lambda s: Tensor(np.zeros(s, dtype=np.float32), device=Device.DEFAULT).contiguous().realize()
# packed npy block (single NPY tensor, mutated in place via views): order matches run_policy.split
shapes = {'desire': (dp[2],), 'traffic_convention': tuple(tc), 'action_t': tuple(at), 'prev_feat': (fb[0], fb[2])}
sizes = [math.prod(s) for s in shapes.values()]
packed = np.zeros(sum(sizes), dtype=np.float32)
@@ -318,7 +315,6 @@ class TinygradSupercomboRunner(ModelRunner):
self._npy['traffic_convention'][:] = numpy_inputs['traffic_convention']
if 'action_t' in numpy_inputs:
self._npy['action_t'][:] = numpy_inputs['action_t']
# self._npy['prev_feat'] holds last frame's hidden_state (zeros on the first frame)
frame = self._frame_tensor('img', bufs['img'])
big_frame = self._frame_tensor('big_img', bufs['big_img'])
@@ -334,11 +330,10 @@ class TinygradSupercomboRunner(ModelRunner):
raise
flat = out.numpy().flatten()
# feed hidden_state back as prev_feat for the next frame
self._npy['prev_feat'][:] = flat[self._hidden_slice].reshape(self._npy['prev_feat'].shape)
sliced = {k: flat[np.newaxis, sl] for k, sl in self._slices.items()}
return self._parser.parse_vision_outputs(sliced) # single-pass; parse_outputs double-parses a combined dict
return self._parser.parse_vision_outputs(sliced)
def _run_model(self) -> NumpyDict:
raise RuntimeError("supercombo path goes through run_fused(), not _run_model()")

2
iqpilot/selfdrive/iqmodeld/models/runners/tinygrad/tinygrad_runner.py
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@@ -85,8 +85,6 @@ class TinygradRunner(ModelRunner, SupercomboTinygrad, PolicyTinygrad, VisionTiny
self.model_run = _load_program_blob(asset_name)
self._input_plan = _compile_input_plan(self.model_run.captured)
# the warp pipeline hands the runner raw uint8 YUV; a float image interface
# would silently reinterpret those bytes and drive on garbage vision
for name, spec in self._input_plan.items():
if "img" in name and spec.dtype is not dtypes.uint8:
raise ValueError(f"{asset_name}: image input {name} expects {spec.dtype}, incompatible with uint8 warp buffer")

7
iqpilot/selfdrive/iqmodeld/models/split_model_constants.py
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@@ -1,4 +1,3 @@
# openpilot model I/O constants (comma.ai, MIT — see LICENSE)
import numpy as np
@@ -7,7 +6,6 @@ def index_function(idx, max_val=192, max_idx=32):
class SplitModelConstants:
# time and distance indices
IDX_N = 33
T_IDXS = [index_function(idx, max_val=10.0) for idx in range(IDX_N)]
X_IDXS = [index_function(idx, max_val=192.0) for idx in range(IDX_N)]
@@ -15,7 +13,6 @@ class SplitModelConstants:
LEAD_T_OFFSETS = [0., 2., 4.]
META_T_IDXS = [2., 4., 6., 8., 10.]
# split-model temporal / history run parameters
MODEL_FREQ = 20
HISTORY_FREQ = 5
HISTORY_LEN_SECONDS = 5
@@ -31,7 +28,6 @@ class SplitModelConstants:
LATERAL_CONTROL_PARAMS_LEN = 2
PREV_DESIRED_CURV_LEN = 1
# model outputs constants
FCW_THRESHOLDS_5MS2 = np.array([.05, .05, .15, .15, .15], dtype=np.float32)
FCW_THRESHOLDS_3MS2 = np.array([.7, .7], dtype=np.float32)
FCW_5MS2_PROBS_WIDTH = 5
@@ -71,7 +67,6 @@ class SplitModelConstants:
POLY_PATH_DEGREE = 4
# model outputs slices
class Plan:
POSITION = slice(0, 3)
VELOCITY = slice(3, 6)
@@ -82,14 +77,12 @@ class Plan:
class Meta:
ENGAGED = slice(0, 1)
# next 2, 4, 6, 8, 10 seconds
GAS_DISENGAGE = slice(1, 31, 6)
BRAKE_DISENGAGE = slice(2, 31, 6)
STEER_OVERRIDE = slice(3, 31, 6)
HARD_BRAKE_3 = slice(4, 31, 6)
HARD_BRAKE_4 = slice(5, 31, 6)
HARD_BRAKE_5 = slice(6, 31, 6)
# next 0, 2, 4, 6, 8, 10 seconds
GAS_PRESS = slice(31, 55, 4)
BRAKE_PRESS = slice(32, 55, 4)
LEFT_BLINKER = slice(33, 55, 4)